Gene Expression Genomic Characterization of Multiple Clinical Phenotypes of Cancer Using Multivariate Linear Regression Models

Motivation: The development of gene expression microarray tech-nology has allowed the identification of differentially expressed genes between different clinical phenotypic classes of cancer from a large pool of candidate genes. Although many class comparisons concerned only a single phenotype, simultaneous assessment of the relationship between gene expression and multiple phenotypes would be warranted to better understand the underlying biological structure. Results: We develop a method to select genes related to multiple clinical phenotypes based on a set of multivariate linear regression models. For each gene, we perform model selection based on the doubly-adjusted R-square statistic and use the maximum of this statistic for gene selection. The method can substantially improve the power in gene selection, compared with a conventional method that uses a single model exclusively for gene selection. Application to a bladder cancer study to correlate pre-treatment gene expres-sions with pathological stage and grade is given. The methods would be useful for screening for genes related to multiple clinical phenotypes. Availability: SAS and MATLAB codes are available from author upon request.